Spreading activation in an attractor network with latching dynamics: Automatic semantic priming revisited

Itamar Lerner, Shlomo Bentin, Oren Shriki

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations, we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model's dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments.

Original languageEnglish
Pages (from-to)1339-1382
Number of pages44
JournalCognitive Science
Volume36
Issue number8
DOIs
StatePublished - 1 Nov 2012
Externally publishedYes

Keywords

  • Distributed representations
  • Latching dynamics
  • Neural networks
  • Semantic priming
  • Word recognition

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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